CN114386223A - Real scene-based driving test simulator examination room model creation method - Google Patents
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Abstract
The invention discloses a real scene-based test field model creating method for a driving test simulator, which comprises the steps of obtaining measurement data of a plurality of detectors of a real test field, and fusing, correcting and extracting characteristics of the measurement data to construct a three-dimensional test field model; acquiring geographic environment data around an examination room, and converting the geographic environment data into a three-dimensional terrain curved surface to generate a geographic environment model; importing a three-dimensional examination room model and a geographic environment model, and adjusting the relative position of the models to obtain a simulated examination room model; analyzing key operation control points of the ground marking of each driving test project according to driving test rules, and marking the key operation control points in the simulation test field model to obtain an integral test field model; and adding a dynamic weather model monomer in the whole examination room model, rendering the whole model, and displaying by using a PC (personal computer) and VR (virtual reality) glasses. The examination room model created by the invention has high simulation degree, and the key operation control points during driving examination are marked in the model to provide guidance for the simulated training of trainees.
Description
Technical Field
The invention relates to the technical field of three-dimensional model creation, in particular to a driving test simulator examination room model creation method based on a real scene.
Background
The operation principle of the automobile driving simulator (VDS) is as follows: the driver manipulates the operating member so that the sensor directly connected to the operating member is changed, thereby causing a change in the electric signal. At present, the VR technology is added, and the result calculated by the vehicle dynamic model is sent to the VR display system for graphic display, sound simulation and instrument display, so that the reality and the substitution sense are stronger.
Along with the popularization of automobiles, the number of people who test the driver licenses is more and more, a large amount of practical training is needed before driver license examination, generally, one-to-many practical training guidance of coaches is adopted, however, because the field, the vehicle and the coach resources of a driving school are limited, an automobile driving simulator is applied to driver license examination training as a novel practical training system, but a student needs to perform examination room adaptive training before officially performing subject two examination and subject three examination of the driver license examination, generally, the practical vehicle practical adaptive training is adopted, the student needs to arrange more extra time to go to an examination field, or the student is familiar with the examination field by dictation of the coach, so that the adaptive training effect of the student is poor, and therefore, a test room model creation method of the driving simulator based on real examination room collection is necessary to be provided.
Disclosure of Invention
The invention provides an examination room model creating method of a driving examination simulator based on a real scene, which is characterized in that an examination room model is created by collecting measurement data of a real examination room and acquiring geographic environment data around the examination room, the model is close to the real examination room, the simulation degree is high, meanwhile, a driving examination rule is combined, key operation control points in the model are labeled, reference guidance is provided for a student, and the learning of the environment of the examination room is facilitated.
The technical scheme of the invention is as follows:
a driving test simulator examination room model creation method based on real examination room collection comprises the following steps:
acquiring measurement data of a plurality of detectors of a real examination room, and fusing, correcting and extracting characteristics of the measurement data to construct a three-dimensional examination room model;
acquiring geographic environment data around the examination room, and converting the geographic environment data into a three-dimensional terrain curved surface to generate a geographic environment model;
step three, importing a three-dimensional examination room model and a geographic environment model, and adjusting the relative position of the models to obtain a simulated examination room model;
analyzing key operation control points of the ground marking of each driving test project according to driving test rules, and marking the key operation control points in the simulation test field model to obtain an integral test field model;
and fifthly, adding a dynamic weather model monomer in the whole examination room model, rendering the whole model, and displaying by using PC and VR glasses in a mode of an HTML5 webpage end or a client end.
Preferably, the detector comprises a satellite navigator, an inertial measurement unit, a laser scanner, an area-array camera and a panoramic camera; the measurement data comprises target ground object point cloud data, image data, detector pose and position data.
Preferably, the first step comprises:
step a, fusing target ground object point cloud data, image data, detector pose and position data to generate color point cloud data under an absolute coordinate system;
b, correcting the color point cloud data by adopting a method combining internal coincidence correction, an iterative closest point algorithm and a target as a control point;
and c, denoising and filtering the corrected color point cloud data by adopting a supervised classification method, dividing the data points into ground points and non-ground points, filtering and separating a plurality of target objects, and performing classification feature extraction, vectorization and three-dimensional scene modeling.
Preferably, the geographic environmental data includes topographic information, elevation information, and satellite maps.
Preferably, the second step includes generating a three-dimensional terrain curved surface around the examination room through Infraworks software, and generating a real environment around the project through Google satellite mapping.
Preferably, step b comprises:
selecting a first scanning feature point as a control point, editing the attribute of the first scanning feature point, and importing a feature point set;
performing curve fitting between the two points by using a least square method, calculating relation parameters and performing in-line correction;
and registering the corrected point clouds by adopting a closest point iterative algorithm, extracting the feature point coordinates corresponding to the control points from the point cloud data by using the target as the control point, and performing space coordinate conversion.
Preferably, the classification feature extraction includes:
extracting ground points, namely extracting point cloud data by adopting a 3 multiplied by 3 dot matrix elevation filtering algorithm to obtain ground point clouds;
extracting a ground mark line, rendering ground point cloud according to a laser reflection intensity value, and obtaining a complete mark line through the region growth of the intensity value;
extracting the upright posts, filtering invalid points by adopting a cluster analysis method according to the spatial distribution characteristics of the point cloud data, comparing the distances between adjacent scanning points with the same angle, selecting point clouds for clustering and growing to obtain the side lines of point cloud angular points and upright posts, performing least square fitting on the point clouds clustered in the same plane, and removing miscellaneous points to obtain the point cloud data of the complete upright posts;
and (5) extracting a vertical face, namely extracting vertical face point cloud according to the projection density of the scanning lines and the point cloud on the horizontal plane.
Preferably, the vectorization and three-dimensional scene modeling processes are:
establishing a topological relation for the generated point cloud model according to the points, lines and surfaces extracted according to the classification features, and extracting contour lines by using the feature points and the feature lines to construct the geometric information of the entity;
and expressing the entity curved surface by using a polygonal mesh or a NURBS curved surface and a triangular Bezier to obtain the geometric information and attribute information of each entity, and performing coordinate conversion and splicing on each entity with a unique identification code to realize a structured three-dimensional model of the entity.
Preferably, the key operating control points include a steering point, a shift point, a brake point, and a lane change point.
Preferably, a reference operation instruction marking the key operation control point is further included.
The invention has the beneficial effects that:
the method provided by the invention has the advantages that a three-dimensional test room model is built by collecting measurement data of a real test room through fusion and correction, a geographic environment model is created by obtaining geographic environment data around the test room, the test room model is obtained through splicing, the model is close to the real test room, the simulation degree is high, meanwhile, the driving test rule is combined, and key operation control points in the model are labeled, so that reference guidance is provided for students, and the method is favorable for proficient assessment of key points and familiarity with test room environment.
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Fig. 1 is a flow chart of an examination room model creating method of a driving examination simulator based on real examination room collection provided by the invention.
Detailed Description
The present invention is described in terms of particular embodiments, other advantages and features of the invention will become apparent to those skilled in the art from the following disclosure, and it is to be understood that the described embodiments are merely exemplary of the invention and that it is not intended to limit the invention to the particular embodiments disclosed. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "in" and the like refer to directions or positional relationships based on those shown in the drawings, which are for convenience of description only, and do not indicate or imply that a device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Furthermore, it should be noted that, in the description of the present invention, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; may be a mechanical connection; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
As shown in fig. 1, an examination room model creation method of a driving examination simulator based on real examination room acquisition includes:
s110, obtaining measurement data of a plurality of detectors of a real examination room, and fusing, correcting and extracting characteristics of the measurement data to construct a three-dimensional examination room model.
The detector comprises a satellite navigator, an inertial measurement instrument, a laser scanner, an area-array camera and a panoramic camera; the measurement data comprises target ground object point cloud data, image data, detector pose and position data.
Firstly, fusing target ground object point cloud, image data detector pose and position data to generate color point cloud data under an absolute coordinate system.
And then correcting the color point cloud data by adopting a method combining internal coincidence correction, an iterative closest point algorithm and a target as a control point.
And performing internal coincidence correction on the point cloud based on time and mileage, performing registration by using an iterative closest point algorithm, and performing coordinate unified conversion by using the target as a control point.
The point cloud correction based on time and mileage is to select corner points or edges of a structure (building) from point cloud data as feature points, calculate an approximate curved surface Z (r (x, y)) of the point cloud of the field where any point is located by utilizing the curved surface curvature of the field points where the point cloud is located, and calculate the micro-deviation r of the approximate curved surface Z (r (x, y)) of the point cloud of the field where any point is locatedx、ry、rxx、ryy、rxyThe rate of change of direction is indicated. The calculation formula for extracting the feature points by the normal vector of the curved surface unit is as follows:
using the gaussian curvature and the mean curvature definition of the curved surface, we can obtain:
where K denotes a gaussian curvature, H denotes an average curvature, and E ═ rxry，F＝ryry，L＝rxrxn，N＝ryryn，M＝rxryn，G＝rxry。
Two main curvatures of the curved surface at the characteristic points can be obtained by the Gaussian curvature K and the average curvature H:
according to the characteristic that most of the characteristic points are inflection points, the obvious curvature change is point cloud information of the characteristic points, and the specific technical steps are as follows:
selecting a first scanning feature point as a control point, editing the attribute of the first scanning feature point, and importing a feature point set;
performing curve fitting between the two points by using a least square method, calculating relation parameters and performing in-line correction;
and accurately registering the corrected point clouds by adopting a closest point iterative algorithm, extracting the feature point coordinates corresponding to the control points from the point cloud data by using the target as the control point, and performing space coordinates.
And finally, denoising and filtering the corrected color point cloud data by adopting a supervised classification method, dividing the data points into ground points and non-ground points, filtering and separating a plurality of target objects, and performing classification feature extraction, vectorization and three-dimensional scene modeling.
Ground point extraction:
in order to realize ground modeling, ground points need to be extracted from the point cloud data, and the point cloud data is extracted by using two characteristics that the ground points are approximate to a horizontal plane and are the lowest points in a certain range and adopting a 3 x 3 dot matrix elevation filtering algorithm to obtain ground point cloud.
Firstly, selecting Z < Z from point cloud data*The point of (2) constitutes a point set P, where any laser point of P is set as a local point, and two points adjacent to the local point on the right and left on the same scanning line, a point closest to the local point on the previous scanning line and the next scanning line, and two points adjacent to the local point on the right and left constitute a 3 × 3 lattice.
Calculating the convolution sum of the Z values of the lattice;
wherein Z is*Indicating an elevation threshold, Z0Z value of scanning coordinate system representing the point, Z1-Z8Scanning the Z value of the coordinate system for the laser of the adjacent point;
setting a threshold value for the sigma Z, and if the threshold value is smaller than the threshold value, marking all points in the dot matrix;
traversing all the laser points in the point set P, not processing the marked points, and repeating the above operations if the marked points are not marked, so as to obtain the ground points after the refined extraction.
Extracting a ground sign line:
the reflection intensity value is an important quantitative value obtained by the laser scanner when the position information is obtained, each laser point corresponds to an intensity value i, and the values of i of laser points falling on different objects are different. And rendering the ground laser points according to the intensity values, and obtaining a complete mark line through the region growth of the intensity values.
Column extraction:
according to the spatial distribution characteristics of the point cloud data, invalid points are filtered out by adopting a cluster analysis method, distances of adjacent scanning points at the same angle are compared, point clouds are selected for clustering and growing to obtain side lines of point cloud angular points and vertical cylindrical surfaces, the point clouds clustered in the same plane are subjected to least square fitting, and miscellaneous points are removed to obtain the point cloud data of the complete vertical cylindrical surfaces.
Extracting a vertical face: and extracting the vertical plane point cloud according to the projection density of the scanning lines and the point cloud on the horizontal plane.
Establishing a topological relation for the generated point cloud model according to the points, lines and surfaces extracted according to the classification features, and extracting contour lines by using the feature points and the feature lines to construct the geometric information of the entity;
and expressing the entity curved surface by using a polygonal mesh or a NURBS curved surface and a triangular Bezier to obtain the geometric information and attribute information of each entity, and performing coordinate conversion and splicing on each entity with a unique identification code to realize a structured three-dimensional model of the entity.
And S120, acquiring geographic environment data around the examination room, and converting the geographic environment data into a three-dimensional terrain curved surface to generate a geographic environment model.
The geographic environment data comprises landform information, elevation information and a satellite map.
And generating a three-dimensional terrain curved surface around the examination room through Infraworks software, and generating a real environment around the project through Google satellite mapping.
And S130, importing the three-dimensional test room model and the geographic environment model, and adjusting the relative position of the models to obtain a simulated test room model.
And S140, analyzing key operation control points of the ground marking of each driving test project according to the driving test rules, and marking the key operation control points in the simulation test field model to obtain the whole test field model.
Wherein the key operating control points include a steering point, a shift point, a brake point, and a lane change point.
A preferred embodiment further comprises a reference operation instruction marking the key operation control point.
In the examination of the driving license subject II, a plurality of items such as backing-up and warehousing, slope driving, curve driving, side parking and the like are generally included, and in the examination items, a coach can summarize some key operation control points and reference operation instructions thereof according to driving and examination experiences.
For example, when a vehicle is parked at a side position, firstly, the vehicle body and the side line of the ground parking space are kept to run for 30cm, a running line can be marked at the distance of 30cm beside the side line of the parking space, or a running arrow is marked at the distance of 30cm plus half of the wheel base of the vehicle body, and a reference operation instruction is marked as that a front central point of the vehicle is coincident with the arrow, so that the distance between the vehicle body and the side line of the parking space can be 30cm only by ensuring that the central point of a front windshield is consistent with the running arrow when a student drives the vehicle; the forward gear is moved straight until the shoulders are overlapped with the stop line and then the vehicle stops, the reverse gear is hung for driving until the vehicle door handle and the garage right angle are observed in the right rear view mirror to vertically make a circle to the right, so that the garage right angle can be used as a key operation control point, and a reference operation instruction is marked as 'the vehicle door handle is overlapped with the point, and the steering wheel turns a circle again'; and (4) reversing gear driving until the left rearview mirror returns to the positive direction after the garage left position angle appears, so that the garage left position angle can be used as a key operation control point, the reference operation instruction is marked as the positive direction returning, the reversing gear driving is continued until the direction is fully filled to the left by rolling the garage position side line by the wheels, and the left rearview mirror can be stopped when the body and the side line are parallel.
The key operation control points and the reference operation instructions thereof are labeled, so that the trainee can master the examination driving skill as soon as possible, the familiarity of the examination room is improved, and the simulated training is beneficial to reducing the tension of the trainee in the examination of the real examination room.
S150, adding a dynamic weather model monomer in the whole examination room model, rendering the whole model, and displaying by using PC and VR glasses in a mode of an HTML5 webpage end or a client end.
By setting parameters such as sunlight, wind, fog and rainfall, the device can be attached to the actual weather environment of an examination room area, the condition that the weather change on the day of examination influences the emotion of a student is avoided, the student can adapt to various weather types in advance, and random strain on the examination site is facilitated.
The above descriptions are only examples of the present invention, and common general knowledge of known specific structures, characteristics, and the like in the schemes is not described herein too much, and it is easily understood by those skilled in the art that the scope of the present invention is obviously not limited to these specific embodiments. Without departing from the invention, several changes and modifications can be made, which should also be regarded as the protection scope of the invention, and these will not affect the effect of the invention and the practicality of the patent.
Claims (10)
1. A driving test simulator examination room model creation method based on real examination room collection is characterized by comprising the following steps:
acquiring measurement data of a plurality of detectors of a real examination room, and fusing, correcting and extracting characteristics of the measurement data to construct a three-dimensional examination room model;
acquiring geographic environment data around the examination room, and converting the geographic environment data into a three-dimensional terrain curved surface to generate a geographic environment model;
thirdly, importing the three-dimensional examination room model and the geographic environment model, and adjusting the relative position of the models to obtain a simulated examination room model;
analyzing key operation control points of the ground marking of each driving test project according to driving test rules, and marking the key operation control points in the simulation test field model to obtain an integral test field model;
and fifthly, adding a dynamic weather model monomer into the whole examination room model, rendering the whole model, and displaying by using PC and VR glasses in a mode of an HTML5 webpage end or a client end.
2. The driving exam simulator examination room model creation method based on real examination room acquisition, according to claim 1, wherein the detector comprises a satellite navigator, an inertial measurement unit, a laser scanner, an area-array camera and a panoramic camera; the measurement data comprises target ground object point cloud data, image data, detector pose and position data.
3. The driving test simulator test room model creation method based on real test room acquisition as claimed in claim 2, wherein the step one comprises:
step a, fusing the target ground object point cloud data, the image data, the detector pose and the position data to generate color point cloud data under an absolute coordinate system;
b, correcting the color point cloud data by adopting a method combining internal coincidence correction, an iterative closest point algorithm and a target as a control point;
and c, denoising and filtering the corrected color point cloud data by adopting a supervised classification method, dividing the data points into ground points and non-ground points, filtering and separating a plurality of target objects, and performing classification feature extraction, vectorization and three-dimensional scene modeling.
4. The driving exam simulator examination room model creation method based on real examination room acquisition, according to claim 3, wherein the geographic environment data comprises geomorphic information, elevation information and satellite maps.
5. The method as claimed in claim 4, wherein the second step includes generating three-dimensional terrain curved surface around the test field by using Infraworks software, and generating real environment around the project by using Google satellite mapping.
6. The driving test simulator test room model creation method based on real test room acquisition as claimed in claim 5, wherein the step b comprises:
selecting a first scanning feature point as a control point, editing the attribute of the first scanning feature point, and importing a feature point set;
performing curve fitting between the two points by using a least square method, calculating relation parameters and performing in-line correction;
and registering the corrected point clouds by adopting a closest point iterative algorithm, extracting the feature point coordinates corresponding to the control points from the point cloud data by using the target as the control point, and performing space coordinate conversion.
7. The driving exam simulator examination room model creation method based on real examination room acquisition as claimed in claim 6, wherein said classification feature extraction comprises:
extracting ground points, namely extracting the point cloud data by adopting a 3 multiplied by 3 dot matrix elevation filtering algorithm to obtain ground point cloud;
extracting a ground mark line, rendering the ground point cloud according to a laser reflection intensity value, and obtaining a complete mark line through the region growth of the intensity value;
extracting the upright posts, filtering invalid points by adopting a cluster analysis method according to the spatial distribution characteristics of the point cloud data, comparing the distances between adjacent scanning points with the same angle, selecting point clouds for clustering and growing to obtain the side lines of point cloud angular points and upright posts, performing least square fitting on the point clouds clustered in the same plane, and removing miscellaneous points to obtain the point cloud data of the complete upright posts;
and (5) extracting a vertical face, namely extracting vertical face point cloud according to the projection density of the scanning lines and the point cloud on the horizontal plane.
8. The driving test simulator test room model creation method based on real test room acquisition as set forth in claim 7, wherein the vectorization and three-dimensional scene modeling process is:
establishing a topological relation for the generated point cloud model according to the points, lines and surfaces extracted according to the classification features, and extracting contour lines by using the feature points and the feature lines to construct the geometric information of the entity;
and expressing the entity curved surface by using a polygonal mesh or a NURBS curved surface and a triangular Bezier to obtain the geometric information and attribute information of each entity, and performing coordinate conversion and splicing on each entity with a unique identification code to realize a structured three-dimensional model of the entity.
9. The driving exam simulator examination room model creation method based on real examination room acquisition, wherein the key operating control points comprise a steering point, a gear shift point, a brake point and a lane change point.
10. The driving exam simulator examination room model creation method based on real examination room acquisition, according to claim 9, further comprising labeling the reference operating instructions of the key operating control points.
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| CN117371949A (en) * | 2023-10-24 | 2024-01-09 | 国网山东省电力公司建设公司 | Three-dimensional visual model-based power transmission line construction safety monitoring method and system |
| CN117371949B (en) * | 2023-10-24 | 2024-05-31 | 国网山东省电力公司建设公司 | Three-dimensional visual model-based power transmission line construction safety monitoring method and system |
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